Analysis of 3D predictions
kanazian
Oct 25, 2020
Goal:
Examine OBmap 3D model predicted positions for concordance with OE DV, FI surface, and other features as well as known OR positions.
Plan:
- Better definition of dorsal-ventral zone based on OE data related to predicted position.
- Define area of functional imaging using wachowiak functional imaging area enriched ORs.
- Annotate positions of Mombaerts ORs onto model and compare predicted positions and distances.
knitr::opts_chunk$set(warning=F, message=F)
library(plotly) ## Loading required package: ggplot2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ tibble 3.0.4 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0
## ✓ purrr 0.3.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks plotly::filter(), stats::filter()
## x dplyr::lag() masks stats::lag()library(cowplot)
library(patchwork)##
## Attaching package: 'patchwork'## The following object is masked from 'package:cowplot':
##
## align_plots# functions -----------------------------------------------------------
#https://stackoverflow.com/questions/49215193/r-error-cant-join-on-because-of-incompatible-types
MatchColClasses <- function(df1, df2) {
sharedColNames <- names(df1)[names(df1) %in% names(df2)]
sharedColTypes <- sapply(df1[,sharedColNames], class)
for (n in sharedColNames) {
class(df2[, n]) <- sharedColTypes[n]
}
return(df2)
}
#define clusters of best X p50 points using a pairwise matrix and ability to output plots and data
#given a number of top ranking positions for an OR, cluster the points based on spatial position
Cluster <- function (olfr, topX = 72, minClustSize = 5,
chooseOut = "plot", title = NA) {
df <- ranked %>%
filter(olfrname == olfr) %>%
mutate(rankofrank = min_rank(desc(min_rank(p50)))) %>%
filter(rankofrank <= topX) %>%
arrange(desc(p50))
cut <- ranked %>%
filter(olfrname == olfr) %>%
mutate(rankofrank = min_rank(desc(min_rank(p50)))) %>%
filter(rankofrank > topX) %>%
arrange(desc(p50))
#make pairwise of matrixes
dmatrix <- matrix(data = 0, nrow = nrow(df), ncol = nrow(df))
for (i in 1:nrow(df)) {
distances <- vector("numeric", length = nrow(df))
toprankvox <- df$voxel[i]
ap <- df$AntPos[i]
ml <- df$MedLat[i]
vd <- df$VenDor[i]
for (j in 1:nrow(df)) {
if (i == j) {
distances[j] <- 0
} else {
distances[j] <- sqrt((df$AntPos[i] - df$AntPos[j])^2 +
(df$MedLat[i] - df$MedLat[j])^2 +
(df$VenDor[i] - df$VenDor[j])^2)
} #endif
} #endforj
neighbors <- which(distances <= sqrt(3))
dmatrix[neighbors, i] <- 1
} #endfori
#cluster matrix
cluster_list <- rep(NA, nrow(df))
for (k in 1:ncol(dmatrix)) {
#skip points that are already in a cluster
if (is.na(cluster_list[k])) {
round <- 1
#do a bunch of rounds, find a way to have it run until it stops finding new points
while (round < topX/3) {
neigh <- which(dmatrix[,k] == 1)
#for each neighbor of previous round of neighbors, find new neighbors
for (l in 1:length(neigh)) {
neigh <- c(neigh, which(dmatrix[,neigh[l]] == 1))
neigh <- neigh[-which(duplicated(neigh))]
neigh <- sort(neigh)
} #endforl
round <- round + 1
} #endwhile
cluster_list[neigh] <- k
} else {
next
} #endif
} #endfork
df_out <- df %>% mutate(rawclust = cluster_list) %>%
group_by(rawclust) %>%
mutate(clustmaxp = max(p50),
clustminp = min(p50),
clustmeanp = mean(p50)) %>%
add_tally() %>%
ungroup() %>%
mutate(clustmaxprank = dense_rank(desc(clustmaxp)),
clustmeanprank = dense_rank(desc(clustmeanp)),
clustsizerank = dense_rank(desc(n))) %>%
arrange(clustsizerank) %>%
mutate(isCluster = ifelse(n >= minClustSize, 1, 0),
clust_unique = dense_rank(desc(clustmaxp * isCluster))) %>%
select(p2.5:ORrankpervox, rankofrank,
rawclust:clustsizerank,
clust_unique, isCluster)
df_clustered <- df_out %>% filter(isCluster == 1)
too_small <- df_out %>% filter(isCluster == 0.5)
cut_out <- cut %>% mutate(rawclust = NA,
clustmaxp = NA,
clustminp = NA,
clustmeanp = NA,
n = NA,
clustmeanprank = NA,
clustmaxprank = NA,
clustsizerank = NA,
isCluster = 0,
clust_unique = NA) %>%
select(p2.5:ORrankpervox,
rankofrank,
rawclust:clustsizerank,
clust_unique,
isCluster) %>%
bind_rows(too_small)
all_out <- bind_rows(df_clustered, cut_out) %>% unique()
#return various things 'rgb(10,10,10)'
if (chooseOut == "data") {
return(all_out)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=cut_out, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_out, x=~AntPos, y=~MedLat, z=~VenDor,
color=~clust_unique,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6,
line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#run cluster given an incrementing number of voxels to cluster from
#needs to also output some sort of summary statistic to define the optimal number of initial voxels that returns the "best" clusters
BestML <- function(olfr, topMin = 50, topMax = 200, topBy = 25, minSize = 2,
clustersPerHalfBulb = 1, chooseOut = "plot", title = NA) {
cphb <- 0
topStep <- topMin
while (cphb < clustersPerHalfBulb) {
df_in <- Cluster(olfr, topX = topStep,
minClustSize = minSize, chooseOut = "data")
df_ml <- df_in %>%
filter(isCluster == 1) %>%
group_by(clust_unique) %>%
mutate(meanML = mean(MedLat),
meanAP = mean(AntPos),
meanVD = mean(VenDor),
side = ifelse(meanML >= symline$mlvals[which(symline$apvals == round(meanAP))],
"Lateral", "Medial")) %>%
ungroup()
df_bestM <- df_ml %>%
filter(side == "Medial") %>%
mutate(MedRank = dense_rank(clust_unique),
LatRank = NA,
TopStep = topStep,
sideRank = MedRank) %>%
filter(MedRank <= clustersPerHalfBulb)
df_bestL <- df_ml %>%
filter(side == "Lateral") %>%
mutate(MedRank = NA,
LatRank = dense_rank(clust_unique),
TopStep = topStep,
sideRank = LatRank) %>%
filter(LatRank <= clustersPerHalfBulb)
clust_bestM <- unique(df_bestM$clust_unique)
clust_bestL <- unique(df_bestL$clust_unique)
#checks and counters
cphb <- min(length(clust_bestM), length(clust_bestL))
topStep <- topStep + topBy
} #endwhile
clust_bestML <- c(clust_bestM, clust_bestL)
which_bestML <- df_in[-which(df_ml$clust_unique %in% clust_bestML),]
df_notbest <- df_in %>%
mutate(sideRank = NA) %>%
select(-clust_unique) %>%
mutate(clust_unique = NA)
df_best <- bind_rows(df_bestM, df_bestL)
df_all <- bind_rows(df_best, df_notbest)
#output
if (chooseOut == "data") {
return(df_all)
} else if (chooseOut == "best") {
return(df_best)
} else if (chooseOut == "notbest") {
return(df_notbest)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=df_notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_best, x=~AntPos, y=~MedLat, z=~VenDor,
color=~clust_unique,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6,
line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#given a list of Olfr names, output 1 medial and 1 lateral cluster for each name
#perhaps add an if or arg for really big lists to run a reduced step
ListML <- function(x, chooseOut = "plot", title = NA) {
#use a list to build a df of unknown size instead of bind_row each iteration
list_out <- vector("list", length = length(x))
for (i in 1:length(x)) {
list_out[[i]] <- BestML(x[i], topMin = 50, topMax = 150, topBy = 50,
minSize = 2, clustersPerHalfBulb = 1, chooseOut = "best")
} #endfor
#always need notbest for the shape shell1
notbest <- BestML(x[1], topMin = 100, topMax = 150,
topBy = 50, minSize = 2,
clustersPerHalfBulb = 1,
chooseOut = "notbest")
df_out <- bind_rows(list_out)
#output
if (chooseOut == "data") {
return(df_out)
} else if (chooseOut == "point") {
df_point <- df_out %>% filter(p50 == clustmaxp)
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_point, x=~AntPos, y=~MedLat, z=~VenDor,
color=~olfrname,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6,
line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_out, x=~AntPos, y=~MedLat, z=~VenDor,
color=~olfrname,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6,
line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#run Cluster and check if either top ranked M or L glom is dorsal and has known dorsal expression or is class 1. If True, find a dorsal glom for both M and L
DorsalML <- function(olfr, topMin = 100, topBy = 100, minSize = 2,
clustIn = 5, clustOut = 1, chooseOut = "plot",
title = NA) {
print(olfr)
clustFound <- 0
topStep <- topMin
while (clustFound != clustOut) {
df_in <- Cluster(olfr, topX = topStep,
minClustSize = minSize, chooseOut = "data")
df_ml <- df_in %>%
filter(isCluster == 1) %>%
group_by(clust_unique) %>%
mutate(meanML = mean(MedLat),
meanAP = mean(AntPos),
meanVD = mean(VenDor)) %>%
ungroup() %>%
rowwise() %>%
mutate(side = ifelse(meanML >= symline$mlvals[which(symline$apvals == round(meanAP))],
"Lateral", "Medial")) %>%
ungroup() %>%
left_join(info, by = "olfrname") %>%
rowwise() %>%
mutate(dorsalRating = sum(ifelse(class == 1, 2, 0),
ifelse(tzsimple < 2, 1, 0),
ifelse(oe_region == "Dorsal", 1, 0),
na.rm = T)) %>%
ungroup()
df_bestM <- df_ml %>%
filter(side == "Medial") %>%
mutate(MedRank = dense_rank(clust_unique),
LatRank = NA,
TopStep = topStep,
minSize = minSize,
clustIn = clustIn,
sideRank = MedRank) %>%
filter(MedRank <= clustIn) %>%
arrange(MedRank)
df_bestL <- df_ml %>%
filter(side == "Lateral") %>%
mutate(MedRank = NA,
LatRank = dense_rank(clust_unique),
TopStep = topStep,
minSize = minSize,
clustIn = clustIn,
sideRank = LatRank) %>%
filter(LatRank <= clustIn) %>%
arrange(LatRank)
max <- max(c(df_bestM$meanVD[1], df_bestL$meanVD[1]))
min <- min(c(df_bestM$meanVD[1], df_bestL$meanVD[1]))
#would be nice to remake using dplyr::case_when()
if (is.na(max)) {
print(paste("NA", as.character(topStep)))
topStep <- topStep + topBy
next
} else {
if (max >= 13) {
if (min < 13) {
if (df_bestM$dorsalRating[1] >= 2) {
#OR has dorsal info, constrain both med/lat to have dorsal glom
if (df_bestM$meanVD[1] == max) {
#Medial was dorsal, find dorsal lateral glom and viceversa
df_bestM <- df_bestM %>%
filter(MedRank == 1) %>% mutate(test = "1m")
df_bestL <- df_bestL %>%
filter(meanVD >= 13) %>%
filter(LatRank == min(LatRank)) %>% mutate(test = "1m")
} else if (df_bestL$meanVD[1] == max) {
df_bestL <- df_bestL %>%
filter(LatRank == 1) %>% mutate(test = "1l")
df_bestM <- df_bestM %>%
filter(meanVD >= 13) %>%
filter(MedRank == min(MedRank)) %>% mutate(test = "1l")
} #endif df_bestM$meanVD[1] == max
} else if (df_bestM$dorsalRating[1] == 1) {
#OR has mixed info, return best
df_bestM <- df_bestM %>%
filter(MedRank == 1) %>%
mutate(test = "2")
df_bestL <- df_bestL %>%
filter(LatRank == 1) %>%
mutate(test = "2")
} else {
#OR has ventral info, constrain both med/lat to have ventral glom
if (df_bestM$meanVD[1] == min) {
#Medial was ventral, find ventral ateral glom and viceversa
df_bestM <- df_bestM %>%
filter(MedRank == 1) %>% mutate(test = "3m")
df_bestL <- df_bestL %>%
filter(meanVD < 13) %>%
filter(LatRank == min(LatRank)) %>% mutate(test = "3m")
} else if (df_bestL$meanVD[1] == min) {
df_bestL <- df_bestL %>%
filter(LatRank == 1) %>% mutate(test = "3l")
df_bestM <- df_bestM %>%
filter(meanVD < 13) %>%
filter(MedRank == min(MedRank)) %>% mutate(test = "3l")
} #endif (df_bestM$meanVD[1] == min)
} #endif (df_bestM$dorsalRating[1] >= 2)
} #endif (min < 13)
} else {
#both gloms are ventral, check if dorsal info
if (df_bestM$dorsalRating[1] >= 2) {
#both gloms are ventral, but OR has dorsal info, constrain to dorsal
df_bestM <- df_bestM %>%
filter(meanVD >= 13) %>%
filter(MedRank == min(MedRank)) %>% mutate(test = "4")
df_bestL <- df_bestL %>%
filter(meanVD >= 13) %>%
filter(LatRank == min(LatRank)) %>% mutate(test = "4")
} else {
df_bestM <- df_bestM %>% filter(MedRank == 1) %>% mutate(test = "5")
df_bestL <- df_bestL %>% filter(LatRank == 1) %>% mutate(test = "5")
} #endif (df_bestM$dorsalRating[1] >= 2)
} #endif (max >= 13)
} #endif (is.nax(max))
df_bestM <- df_bestM %>%
filter(MedRank == min(MedRank)) %>%
mutate(test = "suck")
df_bestL <- df_bestL %>%
filter(LatRank == min(LatRank)) %>%
mutate(test = "suck")
clust_bestM <- unique(df_bestM$clust_unique)
clust_bestL <- unique(df_bestL$clust_unique)
#checks and counters
clustFound <- min(c(length(clust_bestM), length(clust_bestL)))
print(topStep)
topStep <- topStep + topBy
clustIn <- round(clustIn * 1.5)
} #endwhile
clust_bestML <- c(clust_bestM, clust_bestL)
which_bestML <- df_in[-which(df_ml$clust_unique %in% clust_bestML),]
df_best <- bind_rows(df_bestM, df_bestL) %>% unique()
df_notbest <- df_in %>%
mutate(sideRank = NA) %>%
select(-clust_unique) %>%
mutate(clust_unique = NA) %>%
unique()
df_all <- bind_rows(df_best, df_notbest) %>% unique()
#output
if (chooseOut == "data") {
return(df_all)
} else if (chooseOut == "best") {
return(df_best)
} else if (chooseOut == "notbest") {
return(df_notbest)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=df_notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_best, x=~AntPos, y=~MedLat, z=~VenDor,
color=~clust_unique,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6,
line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#given a list of Olfr names, output 1 medial and 1 lateral cluster for each name
ListDorML <- function(x, chooseOut = "plot", title = NA) {
#use a list to build a df of unknown size instead of bind_row each iteration
list_out <- vector("list", length = length(x))
for (i in 1:length(x)) {
list_out[[i]] <- DorsalML(x[i], topBy = 200, chooseOut = "best")
} #endfor
#always need notbest for the shape shell1
notbest <- DorsalML(x[1], chooseOut = "notbest")
df_out <- bind_rows(list_out)
#output
if (chooseOut == "data") {
return(df_out)
} else if (chooseOut == "point") {
df_point <- df_out %>% filter(p50 == clustmaxp)
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_point, x=~AntPos, y=~MedLat, z=~VenDor,
color=~olfrname,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6,
line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} else {
p <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=notbest, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_p50:', clustmeanp,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique),
marker = list(size = 6)) %>%
add_trace(data=df_out, x=~AntPos, y=~MedLat, z=~VenDor,
color=~olfrname,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6,
line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(p)
} #endif
} #endfunction
#using heatmap peak data, create a 3d raw point or find nearest blank OB shell point to that raw point, options to use tan zone to assign a VD position or use average of VD peaks
Heat3D <- function(olfr, dimrep = 1, dv = "oe", raw = F, chooseOut = "plot") {
df_in <- heatmap_peaks %>% filter(olfrname %in% olfr) %>% arrange(side)
if (nrow(df_in) == 0) {
print(paste("No", olfr, "Found"))
next
} #endif
if (dv == "oe") {
df_dv <- df_in %>%
mutate(VD_selected = VDtz,
VD_value = "Tan Based")
} else {
df_dv <- df_in %>%
mutate(VD_selected = VDavg,
VD_value = "Avg Based")
} #endif
if (dimrep == 1) {
df_out <- df_dv %>%
select(olfrname, AntPos10, APval10, MedLat8, MLval8,
VD_selected, VD_value, side) %>%
rename(AP_selected = AntPos10, AP_value = APval10,
ML_selected = MedLat8, ML_value = MLval8)
} else if (dimrep == 2) {
df_out <- df_dv %>%
select(olfrname, AntPos13, APval13, MedLat11, MLval11,
VD_selected, VD_value, side) %>%
rename(AP_selected = AntPos13, AP_value = APval13,
ML_selected = MedLat11, ML_value = MLval11)
} else if (dimrep == 3) {
df_out <- df_dv %>%
select(olfrname, AntPos15, APval15, MedLat16, MLval16,
VD_selected, VD_value, side) %>%
rename(AP_selected = AntPos15, AP_value = APval15,
ML_selected = MedLat16, ML_value = MLval16)
} else if (dimrep == "123") {
df_out <- df_dv %>% rowwise() %>%
mutate(AP_selected = mean(c(AntPos10, AntPos13, AntPos15)),
AP_value = mean(c(APval10, APval13, APval15)),
ML_selected = mean(c(MedLat8, MedLat11, MedLat16)),
ML_value = mean(c(MLval8, MLval11, MLval16))) %>%
ungroup()
} else if (dimrep == "12") {
df_out <- df_dv %>% rowwise() %>%
mutate(AP_selected = mean(c(AntPos10, AntPos13)),
AP_value = mean(c(APval10, APval13)),
ML_selected = mean(c(MedLat8, MedLat11)),
ML_value = mean(c(MLval8, MLval11))) %>%
ungroup()
} #endif
if (raw == T) {
if (chooseOut == "plot") {
plot <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data = blankdata,
x = ~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('AntPos:', AntPos,
'<br>MedLat:', MedLat,
'<br>VenDor:', VenDor),
marker = list(size = 6)) %>%
add_trace(data = df_out,
x = ~AP_selected,
y = ~ML_selected,
z = ~VD_selected,
color = ~olfrname,
text = ~paste('Gene:', olfrname,
'<br>AntPos:', AP_selected,
'<br>APvalue', AP_value,
'<br>MedLat:', ML_selected,
'<br>MLvalue:', ML_value,
'<br>VenDor:', VD_selected,
'<br>VDvalue:', VD_value,
'<br>Side:', side,
'<br>Point:', "Raw"),
marker = list(size = 6,
line = list(color='black', width = 0.5))) %>%
layout(scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(plot)
} else if (chooseOut == "data") {
return(df_out)
}
} else if (raw == F) {
df_close <- tibble("AntPos" = numeric(),
"MedLat" = numeric(),
"VenDor" = numeric())
for (i in 1:nrow(df_out)) {
distances <- vector(mode = "numeric", length = nrow(blankdata))
sidevec <- c("Lateral", "Medial")
for (j in 1:nrow(blankdata)) {
distances[j] <- sqrt((df_out$AP_selected[i] - blankdata$AntPos[j])^2 +
(df_out$ML_selected[i] - blankdata$MedLat[j])^2 +
(df_out$VD_selected[i] - blankdata$VenDor[j])^2)
df_close[i,] <- blankdata[which(rank(distances,
ties.method = "first") == 1),]
}
}
df_close_out <- df_close %>%
mutate(side = sidevec) %>%
left_join(df_out, by = "side") %>%
select(olfrname, AntPos, VenDor, MedLat, side, everything())
if (chooseOut == "plot") {
plot <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data = blankdata,
x = ~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
text = ~paste('AntPos:', AntPos,
'<br>MedLat:', MedLat,
'<br>VenDor:', VenDor),
marker = list(size = 6)) %>%
add_trace(data = df_close_out,
x = ~AntPos,
y = ~MedLat,
z = ~VenDor,
color = ~olfrname,
text = ~paste('Gene:', olfrname,
'<br>AntPos:', AP_selected,
'<br>APvalue', AP_value,
'<br>MedLat:', ML_selected,
'<br>MLvalue:', ML_value,
'<br>VenDor:', VD_selected,
'<br>VDvalue:', VD_value,
'<br>Side:', side,
'<br>Point:', "Nearest Shell"),
marker = list(size = 6,
line = list(color='black', width = 0.5))) %>%
layout(scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
return(plot)
} else if (chooseOut == "data") {
return(df_close_out)
} #endif
} #endif
} #endfunction
#compute distances between heatmap peak point and good point data featuring eurovision movie memes
DistHeat3D <- function(olfr_list, heat_dimrep = 1,
heat_dv = "oe", heat_raw = F) {
doubletrouble <- tibble(olfrname = character(), side = character(),
distance = numeric(), ap_ml_vd_3d = character(),
ap_ml_vd_heat = character(), .rows = 0)
for (i in 1:length(olfr_list)) {
heatpoint <- Heat3D(olfr_list[i], dimrep = heat_dimrep,
dv = heat_dv, raw = heat_raw, chooseOut = "data") %>%
select(olfrname, AntPos, MedLat, VenDor, side) %>%
mutate(origin = "heat",
ap_ml_vd_heat = paste(AntPos, MedLat, VenDor,sep = "_"))
goodpoint <- good_point %>%
filter(olfrname == olfr_list[i]) %>%
select(olfrname, AntPos, MedLat, VenDor, side) %>%
mutate(origin = "3d",
ap_ml_vd_3d = paste(AntPos, MedLat, VenDor,sep = "_"))
doubletrouble <- bind_rows(doubletrouble,
left_join(heatpoint, goodpoint,
by = c("olfrname", "side"),
suffix = c("_h", "_3")) %>%
mutate(distance = sqrt((AntPos_h - AntPos_3)^2 +
(MedLat_h - MedLat_3)^2 +
(VenDor_h - VenDor_3)^2)) %>%
select(olfrname, side, distance,
ap_ml_vd_3d, ap_ml_vd_heat))
} #endfor
doubletrouble <- doubletrouble %>%
mutate(dimrep = heat_dimrep,
vd_assignment = heat_dv,
use_raw = heat_raw)
return(doubletrouble)
} #endfunction
#need to comeup with a naming scheme for args that are input into another function
Plot_props <- function(med_in, lat_in, chooseOut = "plots") {
input_df <- Plot_predictions(med_genes = med_in, lat_genes = lat_in,
chooseOut = "side data")
props <- input_df %>%
filter(p50 == clustmaxp) %>%
filter(!is.na(oe_region)) %>%
filter(!is.na(class)) %>%
mutate(isdor = ifelse(oe_region == "Dorsal", T, F),
isven = ifelse(oe_region == "Ventral", T, F),
isc1 = ifelse(class == 1, T, F),
isc2 = ifelse(class == 2, T, F)) %>%
group_by(VenDor) %>%
summarise(count = n(),
dorsal_ORs = sum(isdor),
ventral_ORs = sum(isven),
class1_ORs = sum(isc1),
class2_ORs = sum(isc2),
prop_dor = dorsal_ORs/count,
prop_c1 = class1_ORs/count)
dv_prop_plot <- ggplot(props %>%
pivot_longer(cols = c(dorsal_ORs, ventral_ORs),
names_to = "sums"),
aes(fill = sums, x = VenDor, y = value)) +
geom_bar(position = "fill", stat = "identity") +
ggtitle("Proportion of Dorsal OE Zone ORs") +
xlab("Ventral <<< 100um sections >>> Dorsal") +
coord_flip()
class_prop_plot <- ggplot(props %>%
pivot_longer(cols = c(class1_ORs, class2_ORs),
names_to = "sums"),
aes(fill = sums, x = VenDor, y = value)) +
geom_bar(position = "fill", stat = "identity") +
ggtitle("Proportion of Class 1 ORs") +
xlab("Ventral <<< 100um sections >>> Dorsal") +
coord_flip()
dv_sum_plot <- ggplot(props) +
geom_bar(aes(VenDor, dorsal_ORs), stat = "identity") +
ggtitle("Number of Dorsal OE Zone ORs") +
xlab("Ventral <<< 100um sections >>> Dorsal") +
coord_flip()
class_sum_plot <- ggplot(props) +
geom_bar(aes(VenDor, class1_ORs), stat = "identity") +
ggtitle("Number of Class 1 ORs") +
xlab("Ventral <<< 100um sections >>> Dorsal") +
coord_flip()
prop_plots <- dv_prop_plot + class_prop_plot
number_plots <- dv_sum_plot + class_sum_plot
if (str_detect(tolower(chooseOut), "plot")) {
return(prop_plots)
} else if (str_detect(tolower(chooseOut), "number")) {
return(number_plots)
} else {
return(props)
} #endif
} #endelse
Plot_predictions <- function(med_genes = NA, lat_genes = NA,
bothsides_genes = NA,
varcolor=~olfrname,
chooseOut = "point",
title = NA) {
if (str_detect(tolower(chooseOut), "point")) {
filter_df <- filter_preds %>% filter(olfrname %in% bothsides_genes)
point_df <- filter_df %>% filter(p50 == clustmaxp)
point <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=blankdata, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
marker = list(size = 6)) %>%
add_trace(data=point_df, x=~AntPos, y=~MedLat, z=~VenDor, color=varcolor,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6,
line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
if (str_detect(tolower(chooseOut), "dat")) {
return(point_df)
} else {
return(point)
} #endif
} else if (str_detect(tolower(chooseOut), "clust")) {
filter_df <- filter_preds %>% filter(olfrname %in% bothsides_vector)
clust <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=blankdata, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
marker = list(size = 6)) %>%
add_trace(data=filter_df, x=~AntPos, y=~MedLat, z=~VenDor,
color=~olfrname,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6,
line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
if (str_detect(tolower(chooseOut), "dat")) {
return(filter_df)
} else {
return(clust)
} #endif
} else if (str_detect(tolower(chooseOut), "side")) {
med_points <- filter_preds %>%
filter(side == "Medial") %>% filter(olfrname %in% med_genes)
lat_points <- filter_preds %>%
filter(side == "Lateral") %>% filter(olfrname %in% lat_genes)
side_df <- bind_rows(med_points, lat_points)
side <- plot_ly(type = "scatter3d", mode = "markers") %>%
add_trace(data=blankdata, x=~AntPos, y=~MedLat, z=~VenDor,
color="shell", opacity=0.15,
marker = list(size = 6)) %>%
add_trace(data=side_df, x=~AntPos, y=~MedLat, z=~VenDor, color=varcolor,
text = ~paste('Gene:', olfrname,
'<br>C_size_rank:', clustsizerank,
'<br>C_mean_ML:', meanML,
'<br>C_max_p50:', clustmaxp,
'<br>Cluster:', clust_unique,
'<br>SideRank:', sideRank),
marker = list(size = 6,
line = list(color = 'black', width = 0.5))) %>%
layout(title = title,
scene = list(xaxis = list(title = 'Anterior-Posterior'),
yaxis = list(title = 'Medial-Lateral'),
zaxis = list(title = 'Ventral-Dorsal')))
if (str_detect(tolower(chooseOut), "dat")) {
return(side_df)
} else {
return(side)
} #endif
} #endif chooseOut
} #endfunctionPlot_props(med_in = med_mids, Plot_props(med_in = med_mids, Plot_props(med_in = med_mids, Plot_props(med_in = med_mids, Plot_props(med_in = med_mids,
### Outlier analysis
#find outliers in terms of DV voxel and DV index (tan, luis, etc.) relationship
#index is a colname, using {{colname}} to pass colname as function arg
Find_DVoutliers <- function(index, sd_from_mean = 1.645) {
for (i in min(filter_preds$VenDor):max(filter_preds$VenDor)) {
filtered_lat <- filter_preds %>%
filter(p50 == clustmaxp) %>%
filter(side == "Lateral") %>%
filter(VenDor == i) %>%
mutate(temp_idx = {{index}})
lat_sum <- filtered_lat %>%
summarise(avg_idx = mean(temp_idx), sd_idx = sd(temp_idx))
lat_midsdlo <- lat_sum$avg_idx[1] - sd_from_mean * lat_sum$sd_idx[1]
lat_midsdhi <- lat_sum$avg_idx[1] + sd_from_mean *lat_sum$sd_idx[1]
filtered_lat <- filtered_lat %>%
mutate(checkhi = ifelse({{index}} > lat_midsdhi, 500, 0),
checklo = ifelse({{index}} < lat_midsdlo, 50, 0),
checkmid = ifelse(between({{index}},
lat_midsdlo, lat_midsdhi),
5, 0),
barhi = lat_midsdhi,
barlo = lat_midsdlo,
barmid = lat_sum$avg_idx[1]) %>%
rowwise() %>%
mutate(pack = checkhi + checklo + checkmid) %>%
ungroup() %>%
select(olfrname, side, VenDor, p50, {{index}},
checkhi, checklo, checkmid, barhi, barlo, barmid, pack) %>%
filter(pack > 0)
filtered_med <- filter_preds %>%
filter(p50 == clustmaxp) %>%
filter(side == "Medial") %>%
filter(VenDor == i) %>%
mutate(temp_idx = {{index}})
med_sum <- filtered_med %>% summarise(avg_idx = mean(temp_idx),
sd_idx = sd(temp_idx))
med_midsdlo <- med_sum$avg_idx[1] - sd_from_mean * med_sum$sd_idx[1]
med_midsdhi <- med_sum$avg_idx[1] + sd_from_mean * med_sum$sd_idx[1]
filtered_med <- filtered_med %>%
mutate(checkhi = ifelse({{index}} > med_midsdhi, 500, 0),
checklo = ifelse({{index}} < med_midsdlo, 50, 0),
checkmid = ifelse(between({{index}}, med_midsdlo, med_midsdhi),
5, 0),
barhi = med_midsdhi,
barlo = med_midsdlo,
barmid = med_sum$avg_idx[1]) %>%
rowwise() %>%
mutate(pack = checkhi + checklo + checkmid) %>%
ungroup() %>%
select(olfrname, side, VenDor, p50, {{index}},
checkhi, checklo, checkmid, barhi, barlo, barmid, pack) %>%
filter(pack > 0)
both_latmed <- bind_rows(filtered_lat, filtered_med)
#combine
if (i == max(filter_preds$VenDor)) {
filtered_all <- bind_rows(filtered_all, both_latmed) %>%
mutate(type = ifelse(pack == 500, "ahi",
ifelse(pack == 50, "zlo",
ifelse(pack == 5, "mid", "woops"))))
return(filtered_all)
} else if (i == min(filter_preds$VenDor)) {
filtered_all <- both_latmed
} else {
filtered_all <- bind_rows(filtered_all, both_latmed)
} #endif
} #endfor
} #endfunc
# return plots or data pertaining to DV position vs DV index outliers
Analyze_DVoutliers <- function(df, index, chooseOut = "Lat") {
if(str_detect(tolower(chooseOut), "lat")) {
lat_indiv <- df %>%
filter(side == "Lateral") %>%
ggplot() +
geom_point(aes(VenDor, {{index}}, color = type), alpha = 0.5) +
geom_point(aes(VenDor, barhi), color = "black", shape = 4) +
geom_point(aes(VenDor, barlo), color = "black", shape = 4) +
geom_point(aes(VenDor, barmid), color = "black", shape = 4) +
ggtitle("Lateral predictions") +
theme(legend.position = "none")
lat_groups <- df %>%
filter(side == "Lateral") %>%
group_by(VenDor, type) %>%
summarise(avgp50 = mean(p50),
group_size = n()) %>%
ggplot() +
geom_point(aes(type, avgp50, size = group_size)) +
facet_wrap(~ VenDor) +
theme(legend.position = "none")
lat_plots <- lat_indiv + lat_groups
return(lat_plots)
} else if (str_detect(tolower(chooseOut), "med")) {
med_indiv <- df %>%
filter(side == "Medial") %>%
ggplot() +
geom_point(aes(VenDor, {{index}}, color = type), alpha = 0.5) +
geom_point(aes(VenDor, barhi), color = "black", shape = 4) +
geom_point(aes(VenDor, barlo), color = "black", shape = 4) +
geom_point(aes(VenDor, barmid), color = "black", shape = 4) +
ggtitle("Medial predictions") +
theme(legend.position = "none")
med_groups <- df %>%
filter(side == "Medial") %>%
group_by(VenDor, type) %>%
summarise(avgp50 = mean(p50),
group_size = n()) %>%
ggplot() +
geom_point(aes(type, avgp50, size = group_size)) +
facet_wrap(~ VenDor) +
theme(legend.position = "none")
med_plots <- med_indiv + med_groups
return(med_plots)
} else {
#return tibble with p50 evaluation of outliers
type_stats <- df %>%
group_by(VenDor, side, type) %>%
summarise(type_p50 = mean(p50),
type_hampel_lo = median(p50) - (3 *mad(p50))) %>%
ungroup() %>% rowwise() %>%
mutate(type_name = paste0(VenDor, side, type)) %>%
select(type_name, type_p50, type_hampel_lo)
mid_stats <- df %>%
filter(type == "mid") %>%
group_by(VenDor, side) %>%
summarise(mid_p50 = mean(p50),
mid_min = min(p50),
mid_1mad = mean(p50) - mad(p50)) %>%
ungroup() %>% rowwise() %>%
mutate(mid_name = paste0(VenDor, side)) %>%
select(mid_name, mid_p50, mid_min, mid_1mad)
df_out <- df %>%
group_by(VenDor) %>%
mutate(vd_p50 = mean(p50)) %>%
ungroup() %>%
mutate(type_name = paste0(VenDor, side, type),
mid_name = paste0(VenDor, side)) %>%
left_join(type_stats, by = "type_name") %>%
left_join(mid_stats, by = "mid_name") %>%
mutate(out_below_mid = ifelse(type != "mid",
ifelse(p50 < mid_p50, T, F), F),
out_below_midmin = ifelse(out_below_mid == T,
ifelse(p50 < mid_min, T, F), F),
out_below_1mad = ifelse(out_below_mid == T,
ifelse(p50 < mid_1mad, T, F), F)) %>%
select(-type_name, -mid_name,
-checkhi, -checkmid, -checklo,
-barhi, -barlo, -barmid, -pack)
return(df_out)
} #endif
} #endfuncLoad data
kzY <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/covarintactchemo_over_samples_200923.csv", col_names = TRUE) %>% select(-X1) %>% filter(dimrep == 1 | dimrep == 2)
ectopic <- c("Olfr287","Olfr32")
kzYgood <- select(kzY, -ectopic) %>% select(-name, -rep, -slice, -dim, -dimrep)
kzmY <- as.matrix(kzYgood)
#feature info
info <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/knowntanwavgFI.csv",
col_names = TRUE) %>%
rename("olfrname" = "gene") %>%
select(olfrname:RTP, known, lowTPM)
#line of bulb symmetry as found using single-dimension heatmap data
#the average of the two calculated lines is used to call whether a predicted glomeruli position is medial or lateral
symline <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/symline.csv")
ggplot() +
geom_blank() +
geom_abline(aes(slope = 0.32655, intercept = 7.61119, color = "rep1")) +
geom_abline(aes(slope = 0.24922, intercept = 8.83890, color = "rep2")) +
geom_abline(aes(slope = 0.287885, intercept = 8.225045, color = "mean")) +
xlim(0,23) +
ylim(0,22) +
ggtitle("Glomeruli symmetry line as calculated by single dimension heatmap peaks") +
xlab("Mean position of top 2 AP peaks") +
ylab("Mean position of top 2 ML peaks")
# Example 3D left OB for future orientation of dimensions
#blankdata <- Scat_rank("Olfr881", 1, "data") %>% select(AntPos:VenDor) %>% arrange(AntPos, MedLat, VenDor)
#write_csv(blankdata, "~/Desktop/rproj/obmap/allmice/v21_gen25/base_files/blankOBcoords.csv")
#blankOBcoords200922
blankdata <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/blankOBcoords200922.csv")
dvml_blank <- blankdata %>%
mutate(dimdv = ifelse(VenDor >= 13,
"Dorsal",
"Ventral"),
dimml = ifelse(MedLat > 17,
"Lateral",
ifelse(MedLat < 5,
"Medial",
NA)),
dim = ifelse(is.na(dimml), dimdv, dimml)) %>%
rowwise() %>%
mutate(symdim = ifelse(MedLat == round(symline$mlvals[which(symline$apvals == AntPos)]),"SymLine", dim)) %>%
ungroup()
#blankOB on dark background
plot_ly(dvml_blank, x = ~MedLat, y = ~AntPos, z = ~VenDor,
color = ~VenDor,
text = ~paste('Dim: ', dim,
'<br>AP:', AntPos,
'<br>ML:', MedLat,
'<br>VD:', VenDor),
marker = list(size = 8, symbol = "circle"),
type = 'scatter3d',
mode = 'markers',
hoverinfo = "none",
hovertext="none") %>%
layout(scene = list(xaxis = list(title = 'Medial-Lateral',
backgroundcolor="rgb(200, 200, 230",
gridcolor="rgb(255,255,255)",
showbackground=TRUE,
zerolinecolor="rgb(255,255,255"),
yaxis = list(title = 'Anterior-Posterior',
backgroundcolor="rgb(200, 200, 230",
gridcolor="rgb(255,255,255)",
showbackground=TRUE,
zerolinecolor="rgb(255,255,255)"),
zaxis = list(title = 'Ventral-Dorsal',
backgroundcolor="rgb(200, 200, 230",
gridcolor="rgb(255,255,255)",
showbackground=TRUE,
zerolinecolor="rgb(255,255,255)")))#load 3d model predictions (clusters) from ListDorML function with non-OLfr genes expected to be Dorsal
all_predictions <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/output/allchemo_dornonor_ldML_201012.csv")
#allORs2 <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/output/allchemo_ldML_200925.csv") #%>% filter(p50 == clustmaxp)
#aORcor <- tibble(olfrname = allORs$olfrname, oneAP = allORs$AntPos, oneML = allORs$MedLat, oneVD = allORs$VenDor, twoAP = allORs2$AntPos, twoML = allORs2$MedLat, twoVD = allORs2$VenDor) %>% filter(!str_detect(olfrname, "Olfr")) %>% summarise(corAP = cor(oneAP, twoAP), corML = cor(oneML, twoML), corVD = cor(oneVD, twoVD))
# main change is corVD, due to addition of dorsal rating for VMNrs and TAARsFilter
#get summary statistics for each OR
kzmY[1:5,1:5]## Olfr299 Olfr109 Olfr281 Olfr1015 Olfr1347
## [1,] 0.00 0.00 0.33 5.77 0.0
## [2,] 277.35 0.00 602.80 228.19 0.3
## [3,] 0.00 99.66 2451.54 0.11 0.0
## [4,] 0.00 0.00 1882.25 340.79 0.0
## [5,] 0.00 0.00 2612.72 0.00 0.0ornames <- colnames(kzmY)
max_tpm <- vector(mode = "numeric", length = ncol(kzmY))
mean_tpm <- vector(mode = "numeric", length = ncol(kzmY))
sd_tpm <- vector(mode = "numeric", length = ncol(kzmY))
for (i in 1:ncol(kzmY)) {
max_tpm[i] <- max(kzmY[,i])
mean_tpm[i] <- mean(kzmY[,i])
sd_tpm[i] <- sd(kzmY[,i])
}
# remove ORs with low max/mean values and low max tpm values, can probably do better
metrics <- tibble(ornames, max_tpm, mean_tpm, sd_tpm) %>%
rowwise() %>%
mutate(max2mean = max_tpm/mean_tpm,
max2mean_lo = ifelse(max2mean < 5, T, F),
maxSDmean = (max_tpm - mean_tpm)/sd_tpm,
maxSDmean_lo = ifelse(maxSDmean < 3, T, F),
coefvar = sd_tpm/mean_tpm,
coefvar_lo = ifelse(coefvar < 1, T, F),
maxtpm_lo = ifelse(max_tpm < 10, T, F))
goodORs <- metrics %>%
filter(max2mean_lo == F) %>%
filter(maxSDmean_lo == F) %>%
filter(coefvar_lo == F) %>%
filter(maxtpm_lo == F) %>%
select(ornames) %>%
as_vector()
filter_preds <- all_predictions
filter_olfrs <- filter_preds %>%
filter(str_detect(olfrname, "Olfr")) %>%
select(olfrname) %>% unique() %>% as_vector()
filter_vmnrs <- filter_preds %>%
filter(str_detect(olfrname, "Vmn")) %>%
select(olfrname) %>% unique() %>% as_vector()
filter_taars <- filter_preds %>%
filter(str_detect(olfrname, "Taar")) %>%
select(olfrname) %>% unique() %>% as_vector()
filter_all <- filter_preds %>%
select(olfrname) %>% unique() %>% as_vector()Class 1 vs Class 2 positions
Expect Class 1 OR positions to be primarily dorsal-anterior to dorsal-central. Expect Class 2 ORs to be distributed throughout the OB Filtered out some ORs based on max to mean TPM ratio lower than 10 (968 ORs remaining). These could be ORs that are poorly enriched/dropout or have non-traditional expression across the OB (requires closer investigation).
#lets look at class 1 vs class 2 ORs, note that is it possible for a voxel to hold multiple OR cluster points
filter_preds <- filter_preds %>%
mutate(class_fct = as_factor(class))
Plot_predictions(bothsides_genes = filter_olfrs, varcolor = ~class_fct)OE region positions (zonal expression of OR as determined by Matsunami Lab DiffE)
3 samples of dorsal OE vs 3 samples of ventral OE Could also examine relation to more discrete tan et al. zone indices but this is more readable.
Plot_predictions(bothsides_genes = filter_olfrs,
varcolor = ~oe_region,
title = "OE Zone of cluster points for 1115 ORs")#a look at proportions to deal with point density
Plot_props(med_in = filter_olfrs, lat_in = filter_olfrs)
Tan zone indexes
Note that the tan index here (tzsimplest) is flipped in order to have brighter colors reflect more dorsal index values in accordance with Hiros preferences
filter_preds <- filter_preds %>%
mutate(tzsimplest = ifelse(tzsimple <= 5, 6-tzsimple, NA),
tzbins = round(tzsimplest/0.5)) %>%
filter(!is.na(tzsimplest))
Plot_predictions(bothsides_genes = filter_olfrs, varcolor = ~tzsimplest)Are there position vs index outliers?
#predictions for medial vs lateral are probably something good to discuss in paper
filter_preds %>%
filter(p50 == clustmaxp) %>%
filter(side == "Medial") %>%
ggplot() +
geom_jitter(aes(tzbins, VenDor)) +
geom_smooth(aes(tzbins, VenDor)) +
ggtitle("Medial points - tan and prediction position")
filter_preds %>%
filter(p50 == clustmaxp) %>%
filter(side == "Lateral") %>%
ggplot() +
geom_boxplot(aes(as_factor(tzbins), VenDor)) +
ggtitle("Lateral points - tan and prediction position")
Digging down on outliers
outliers <- Find_DVoutliers(tzsimplest, 1)
Analyze_DVoutliers(outliers, tzsimplest, "med")
Analyze_DVoutliers(outliers, tzsimplest, "lat")
outlier_df <- Analyze_DVoutliers(outliers, tzsimplest, "data")Plot non-outliers using sided gene vectors
med_mids <- outlier_df %>% filter(side == "Medial") %>%
filter(type == "mid") %>%
select(olfrname) %>% as_vector()
lat_mids <- outlier_df %>% filter(side == "Lateral") %>%
filter(type == "mid") %>%
select(olfrname) %>% as_vector()
Plot_predictions(med_genes = med_mids, lat_genes = lat_mids,
varcolor=~tzsimplest,
chooseOut = "side",
title = "ML points - No outliers")Plot_props(med_in = med_mids, lat_in = lat_mids)
Plot_props(med_in = med_mids, lat_in = lat_mids, chooseOut = "number")
Where are outliers located?
med_outs <- outlier_df %>% filter(side == "Medial") %>%
filter(type != "mid") %>%
select(olfrname) %>% as_vector()
lat_outs <- outlier_df %>% filter(side == "Lateral") %>%
filter(type != "mid") %>%
select(olfrname) %>% as_vector()
Plot_predictions(med_genes = med_outs, lat_genes = lat_outs,
varcolor=~tzsimplest,
chooseOut = "side",
title = "ML points - Just outliers")Plot_props(med_in = med_outs, lat_in = lat_outs)
Plot_props(med_in = med_outs, lat_in = lat_outs, chooseOut = "number")
Outliers with probability values below the average probability of the non-outliers
med_belowmid <- outlier_df %>% filter(side == "Medial") %>%
filter(out_below_mid == T) %>%
select(olfrname) %>% as_vector()
lat_belowmid <- outlier_df %>% filter(side == "Lateral") %>%
filter(out_below_mid == T) %>%
select(olfrname) %>% as_vector()
Plot_predictions(med_genes = med_belowmid, lat_genes = lat_belowmid,
varcolor=~tzsimplest,
chooseOut = "side",
title = "ML points - Outliers below mid p50")Plot_props(med_in = med_belowmid, lat_in = lat_belowmid)
Plot_props(med_in = med_belowmid, lat_in = lat_belowmid, chooseOut = "number")
Outliers with probability values below the min probability of the non-outlier group
med_belowmidmin <- outlier_df %>% filter(side == "Medial") %>%
filter(out_below_midmin == T) %>%
select(olfrname) %>% as_vector()
lat_belowmidmin <- outlier_df %>% filter(side == "Lateral") %>%
filter(out_below_midmin == T) %>%
select(olfrname) %>% as_vector()
Plot_predictions(med_genes = med_belowmidmin, lat_genes = lat_belowmidmin,
varcolor=~tzsimplest,
chooseOut = "side",
title = "ML points - Outliers below min mid p50")Plot_props(med_in = med_belowmidmin, lat_in = lat_belowmidmin)
Plot_props(med_in = med_belowmidmin, lat_in = lat_belowmidmin, chooseOut = "number")
Outliers from Luis’ DPT indexes
#olfr1204 had a dpt of 0, changed to 1
ls_idx <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/LS_3Dindexes_real_pred.csv") %>%
mutate(logDPT = log2(DPT_index + 0.1),
rankDPT = min_rank(DPT_index))
filter_preds <- filter_preds %>%
left_join(ls_idx, by = "olfrname")
Plot_predictions(bothsides_genes = filter_olfrs, varcolor = ~logDPT)DPT index outlier analysis
dpt_outs <- Find_DVoutliers(DPT_index)
Analyze_DVoutliers(dpt_outs, DPT_index, chooseOut = "Lat")
Analyze_DVoutliers(dpt_outs, DPT_index, chooseOut = "Med")
dpt_out_df <- Analyze_DVoutliers(dpt_outs, DPT_index, chooseOut = "data")
med_dpt_mids <- dpt_out_df %>% filter(side == "Medial") %>%
filter(type == "mid") %>%
select(olfrname) %>% as_vector()
lat_dpt_mids <- outlier_df %>% filter(side == "Lateral") %>%
filter(type == "mid") %>%
select(olfrname) %>% as_vector()
Plot_predictions(med_genes = med_dpt_mids, lat_genes = lat_dpt_mids,
varcolor = ~DPT_index)Plot_props(med_in = med_dpt_mids, lat_in = lat_dpt_mids)
med_dpt_outs <- dpt_out_df %>% filter(side == "Medial") %>%
filter(type != "mid") %>%
select(olfrname) %>% as_vector()
lat_dpt_outs <- outlier_df %>% filter(side == "Lateral") %>%
filter(type != "mid") %>%
select(olfrname) %>% as_vector()
Plot_predictions(med_genes = med_dpt_outs, lat_genes = lat_dpt_outs,
varcolor = ~DPT_index)Plot_props(med_in = med_dpt_outs, lat_in = lat_dpt_outs)
Plot_props(med_in = med_dpt_outs, lat_in = lat_dpt_outs, chooseOut = "number")
med_dpt_belowmidmin <- dpt_out_df %>% filter(side == "Medial") %>%
filter(out_below_midmin == T) %>%
select(olfrname) %>% as_vector()
lat_dpt_belowmidmin <- outlier_df %>% filter(side == "Lateral") %>%
filter(out_below_midmin == T) %>%
select(olfrname) %>% as_vector()
Plot_predictions(med_genes = med_dpt_belowmidmin, lat_genes = lat_dpt_belowmidmin,
varcolor = ~DPT_index)Plot_props(med_in = med_dpt_belowmidmin, lat_in = lat_dpt_belowmidmin)
Plot_props(med_in = med_dpt_belowmidmin, lat_in = lat_dpt_belowmidmin, chooseOut = "number") 
Topics from Luis 3D OE project
#Mayra/Antonio/Luis topics
topics <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/LS_DegreesOfBelonging_201015.csv")
# topic_max <- topics_raw %>%
# rowwise() %>%
# arrange(desc(T5)) %>%
# mutate(maxval = pmax(T1, T2, T3, T4, T5)) %>%
# pivot_longer(T1:T5, names_to = "topic", values_to = "prob") %>%
# rowwise() %>%
# mutate(max_topic = ifelse(maxval == prob, topic, NA)) %>%
# select(olfrname, max_topic) %>%
# filter(!is.na(max_topic)) %>%
# ungroup()
#
# topics_join <- left_join(topics, topic_max, by = "olfrname")
# write_csv(topics_join, "~/Desktop/obmap/r_analysis/3dimOB/input/LS_DegreesOfBelonging_201015.csv")
filter_preds <- filter_preds %>%
left_join(topics, by = "olfrname") %>%
filter(!is.na(max_topic))
topic_ors <- filter_preds$olfrname
Plot_predictions(topic_ors, varcolor = ~max_topic)topic_X <- filter_preds %>% filter(max_topic == "T2") %>%
select(olfrname) %>% as_vector()
Plot_predictions(topic_X, varcolor = ~max_topic)#1 is all over
#2 is dorsal, class 1 enriched (67 class 1 ORs, 99 class 2 ORs)
#3 is ventral
#4 is only 1 Olfr338, dorsal
#5 is posteriorFind positions for 50 ORs enriched in Wachowiak Functional Imaging surface samples
Shawn Burton provided samples from 8 OBs from 4 mice, each OB cut into 2 pieces with 1 piece representing the functional imaging surface. Using the above algorithm for picking the best medial and lateral cluster for a given OR. Need to update list for newest alignment updates and to include all FI ORs now that code was improved in terms of speed.
olfr_result <- read_csv("~/Desktop/rproj/obmap_inactive/wach_diffe/starrsem_aligned/out/wach_v16model_top25FIenriched.csv") %>%
select(-Used) %>%
mutate(FIsur = ifelse(logFC > 1, ifelse(FDR < 0.05, 1, 0), 0))
func_sig <- olfr_result %>%
filter(FIsur == 1) %>%
filter(!str_detect(Gene_name, "-ps")) %>%
arrange(FDR)
func_sig_olfr <- func_sig$Gene_name
func_sig_olfr## [1] "Olfr376" "Olfr629" "Olfr57" "Olfr1046" "Olfr1122" "Olfr937"
## [7] "Olfr994" "Olfr228" "Olfr570" "Olfr558" "Olfr969" "Olfr402"
## [13] "Olfr683" "Olfr597" "Olfr19" "Olfr578" "Olfr550" "Olfr566"
## [19] "Olfr1496" "Olfr974" "Olfr478" "Olfr677" "Olfr147" "Olfr561"
## [25] "Olfr957" "Olfr971" "Olfr633" "Olfr231" "Olfr1032" "Olfr922"
## [31] "Olfr1023" "Olfr635" "Olfr1031" "Olfr690" "Olfr1010" "Olfr1019"
## [37] "Olfr609" "Olfr874" "Olfr510" "Olfr1134" "Olfr160" "Olfr197"
## [43] "Olfr467" "Olfr150" "Olfr935" "Olfr64" "Olfr5" "Olfr506"
## [49] "Olfr1377" "Olfr338" "Olfr1086" "Olfr1020" "Olfr202" "Olfr1339"
## [55] "Olfr225" "Olfr691" "Olfr895" "Olfr20" "Olfr982" "Olfr146"
## [61] "Olfr1328" "Olfr1129" "Olfr432" "Olfr152" "Olfr1449" "Olfr490"
## [67] "Olfr557" "Olfr51" "Olfr488" "Olfr1448" "Olfr1154" "Olfr1128"
## [73] "Olfr1511" "Olfr881" "Olfr133"ggplot(olfr_result) +
geom_point(aes(logFC,-log10(FDR), alpha = 0.25,
color = as.factor(FIsur), size = 1.3)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = -log10(0.05)) +
theme_cowplot() +
theme(legend.position = "none") +
xlab("nonFIsurface <<< log2FoldChange >>> FIsurface")
Plot significantly enriched FI surface ORs
Best in this case refers to highest FDR (aka how consistency enriched in functional imaging surface). Perhaps color by an adjusted FDR?
#plot only highest p50 voxel of each cluster
Plot_predictions(func_sig_olfr, varcolor=~p50,
chooseOut = "point",
title = "Medial/Lateral points for FI surface enriched ORs")plot heatmap peaks and calc dist to DorML
heatmap_peaks <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/input/heatmap_peaks.csv")
Heat3D("Olfr881", dimrep = 1, dv = "oe", raw = F, chooseOut = "plot")
all_heat_in_good <- heatmap_peaks$olfrname[which(heatmap_peaks$olfrname %in% filter_olfrs)]
#ahig_dist <- DistHeat3D(all_heat_in_good)
#write_csv(ahig_dist, "~/Desktop/rproj/obmap/allmice/v21_gen25/heatmapORs_distance_to3D.csv")
ahig_dist <- read_csv("~/Desktop/obmap/r_analysis/3dimOB/output/heatmapORs_distance_to3D.csv")
ahig_dist %>% ggplot(aes(side, distance)) + geom_violin()Check if new Dorsal constraint function improves output
New function checks if OR has evidence of dorsal OE expression (class 1 or miyamichi zone < 2, matsunami diffE = dorsal). If so, check if either the Medial or Lateral predicted position is dorsal. If OR is likely dorsal and either Medial or Lateral glomerulus prediction is dorsal but other halfbulb glom is not dorsal, find a dorsal glom for that halfbulb In independent images for the more lateral glomerulus, 1377 seems slightly more anterior lateral than 881